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Electricity network pricing methodologies play a key role in determining whether providing network services is economically beneficial to both the utilities and other m arket participants. There are many pricing methodologies that have been developed since the late 80's, especially for transmission networks. Compared to transmission pricing methods, distribution pricing methods are unsophisticated and pose a significant barrier to embedded generators. Hence, more efficient and executable methodologies are still desirable in open access electricity networks. This thesis presents a series of new long-run marginal cost (LRMC) pricing methodologies for both transmission and distribution networks, and demonstrates the processes o f evaluating and allocating the network asset costs. New reactive power pricing methods, based on the perpendicular approach and arc approach, have been proposed and demonstrated in these LRMC pricing process. Compared with other proposed LRMC pricing schemes, the novel long-run marginal cost with utilisation consideration (LRM C-Util%) pricing methodology aims to evaluate the network asset costs based on the usage o f the network facilities. It can reflect the future network investment and indicate the future location o f network users. The advantages o f LRMC-Util% include the ability to reflect the forward looking costs, to distinguish between the costs o f siting at different locations, to recognize o f the reactive power, and to derive charges for both generation and demand users.

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The main objective of the dissertation is to improve the imaging results for magneticinductance tomography (MIT)as anon-destructive imaging technique. MIT is generallyused to display the imaging contains the conductivity properties of the object under test. The hardware and software are regarded as basic topics of the development of MIT. The hardware of MIT is briefly introduced but analyzing the software problem of MIT is the main purposeof this thesis. The working flow of this dissertation can be explained as the following sections. Firstly, the forward problem of MIT hasbeen studied theoretically, including eddy current modeling with Biot-Savart theory implemented and the simulation works for the validation of forward problem. Secondly, the algorithms of inverse problem solvers are presentedbased on the explanation of mathematical equations, including linear/non-linear or iterative/non-iterative inverse problem. Thirdly, improved image quality of reconstructed images obtained by total variation regularization as the inverse problem solver both in circular and planar array sensor MIT system are demonstrated by experimental results. Finally, the potential feasibility of planar MIT system assisting other imaging system such as electrical capacitance tomography for plastic landmine detection is illustrated by simulation works. Altogether, this thesis presents the author’s research interests on improving reconstruction performance of MIT trough analyzingoninverse problem algorithmsdevelopments and expand the potential application of planar low conductivity MIT system in plastic Landmine detection.

Computed tomography (CT), and especially cone-beam computed tomography (CBCT) has a wide range of applications. This thesis focuses on CBCT for image-guided radiation therapy (IGRT), particularly for lung cancer treatment. In lung IGRT the tumour moves due to respiration, not only making it hard to target with the radiation beam, but also blurring the images acquired for daily treatment tuning. Generating high quality images without motion artefacts is essential for {radiation} and hadron therapy. In this thesis, motion modelling ideas from CERN's phase space tomography are modified and adapted to lung CBCT. The CERN method includes a knowledge of the motion in the basic building blocks of the image reconstruction and uses all the acquired data to reconstruct a single static image at any chosen moment within the acquisition timespan. In order to use this method, and in general improve the reconstructed image quality of CBCT, iterative algorithms are explored with a focus on fast reconstruction using GPUs. The work presented here lead to the publication of the TIGRE Toolbox, a fast, easy-to-use MATLAB-CUDA toolbox for the reconstruction of CBCT images at state-of-the-art speeds with an extensive variety of iterative algorithms. This thesis presents the mathematics, GPU techniques and different applications of TIGRE and its algorithms, strengthening the idea already stated that iterative algorithms can significantly improve image quality in CBCT. A motion compensation method is developed together with a fast GPU implementation and its robustness is tested numerically by simulating the expected clinical errors in the data. The method is very robust and provides high-quality static images using data from disparate moments in time, offering the prospect of videos of patients breathing at no extra cost in radiation dose.